Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain
The continual evolution of the SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particu...
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ftpubmed:oai:pubmedcentral.nih.gov:9428596 2023-05-15T16:01:35+02:00 Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max Yermanos, Alexander Kelton, William Reddy, Sai T. 2022-08-31 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428596/ https://doi.org/10.1016/j.cell.2022.08.024 en eng The Author(s). Published by Elsevier Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428596/ http://dx.doi.org/10.1016/j.cell.2022.08.024 © 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Cell Resource Text 2022 ftpubmed https://doi.org/10.1016/j.cell.2022.08.024 2022-09-04T01:10:13Z The continual evolution of the SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19. Text DML PubMed Central (PMC) Cell |
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Resource Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max Yermanos, Alexander Kelton, William Reddy, Sai T. Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain |
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The continual evolution of the SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19. |
format |
Text |
author |
Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max Yermanos, Alexander Kelton, William Reddy, Sai T. |
author_facet |
Taft, Joseph M. Weber, Cédric R. Gao, Beichen Ehling, Roy A. Han, Jiami Frei, Lester Metcalfe, Sean W. Overath, Max Yermanos, Alexander Kelton, William Reddy, Sai T. |
author_sort |
Taft, Joseph M. |
title |
Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain |
title_short |
Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain |
title_full |
Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain |
title_fullStr |
Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain |
title_full_unstemmed |
Deep Mutational Learning Predicts ACE2 Binding and Antibody Escape to Combinatorial Mutations in the SARS-CoV-2 Receptor Binding Domain |
title_sort |
deep mutational learning predicts ace2 binding and antibody escape to combinatorial mutations in the sars-cov-2 receptor binding domain |
publisher |
The Author(s). Published by Elsevier Inc. |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428596/ https://doi.org/10.1016/j.cell.2022.08.024 |
genre |
DML |
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DML |
op_source |
Cell |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428596/ http://dx.doi.org/10.1016/j.cell.2022.08.024 |
op_rights |
© 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
op_doi |
https://doi.org/10.1016/j.cell.2022.08.024 |
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Cell |
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